This course covers some advanced topics including strategies for handling large data sets and specialty plots.

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).In this third ggplot2 course, we'll dive into some advanced topics including geoms commonly used in maths and sciences, strategies for handling large data sets, a variety of specialty plots, and some useful features of ggplot2 internals.

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### Statistical plots

**Free**Actually, all the plots you've explored in the first two ggplot2 courses can be considered 'statistical plots'. Here, however, you'll consider those that are intended for a specialist audience that is familiar with the data: box plots and density plots.

Introduction50 xpAesthetics review50 xpRefresher (1)100 xpRefresher (2)100 xpRefresher (3)100 xpBox Plots50 xpTransformations100 xpCut it up!100 xpUnderstanding quartiles50 xpDensity Plots50 xpgeom_density()100 xpCombine density plots and histogram100 xpAdjusting density plots100 xpMultiple Groups/Variables50 xpBox plots with varying width100 xpMulitple density plots100 xpMultiple density plots (2)100 xpWeighted density plots100 xp2D density plots (1)100 xp2D density plots (2)100 xp - 2
### Plots for specific data types (Part 1)

In this chapter, you'll explore useful specialty plots for specific data types such as ternary plots, networks and maps. You'll also look at how to use ggplot2 to convert typical base package plots that are used to evaluate the results of statistical methods. Finally, you'll take a look at a couple ways in which you can make and appropriately use animations.

Graphics of Large Data50 xpPair plots and correlation matrices100 xpCreate a correlation matrix in ggplot2100 xpTernary Plots50 xpProportional/stacked bar plots100 xpProducing ternary plots100 xpAdjusting ternary plots100 xpNetwork Plots50 xpBuild the network (1)100 xpBuild the network (2)100 xpAdjusting the network100 xpDiagnostic Plots50 xpAutoplot on linear models100 xpggfortify - time series100 xpDistance matrices and Multi-Dimensional Scaling (MDS)100 xpPlotting K-means clustering100 xp - 3
### Plots for specific data types (Part 2)

In this chapter, we'll continue our discussion of plots for specific data types by diving into the world of maps. You'll also have a look at animations to make your data come to life!

Choropleths50 xpWorking with maps from the maps package: USA100 xpWorking with maps from the maps package: adding points100 xpState choropleth100 xpMap from shapefiles100 xpChoropleth from shapefiles100 xpCartographic Maps50 xpDifferent templates100 xpMapping points onto a cartographic map100 xpCombine cartographic and choropleth maps100 xpAnimations50 xpThe population pyramid100 xpAnimations with gganimate100 xp - 4
### ggplot2 Internals

In this chapter, you'll delve into ggplot2 internals, exploring the grid package and ggproto. You'll learn how to use these tools to create unique plots.

Grid Graphics50 xpViewport basics (1)100 xpViewport basics (2)100 xpBuild a plot from scratch (1)100 xpBuild a plot from scratch (2)100 xpModifying a plot with grid.edit100 xpGrid Graphics in ggplot250 xpExploring the gTable100 xpModifying the gTable100 xpggplot Objects50 xpExploring ggplot objects100 xpggplot_build and ggplot_gtable100 xpExtracting details100 xpgridExtra50 xpArranging plots (1)100 xpArranging plots (2)100 xp - 5
### Data Munging and Visualization Case Study

In this chapter, you'll draw on some of the many tools for effective data visualization that we've covered over the three ggplot2 courses and combine them with some data munging techniques.

Case Study I - Bag Plot50 xpBase package bag plot100 xpMultilayer ggplot2 bag plot100 xpCreating ggproto functions100 xpCreating stat_bag()100 xpUse stat_bag()100 xpCase Study II - Weather (Part 1)50 xpStep 1: Read in data and examine100 xpStep 2: Summarize history100 xpStep 3: Plot history100 xpStep 4: Plot present100 xpStep 5: Find new record highs100 xpStep 6: Efficiently calculate record highs and lows100 xpStep 7: Custom legend100 xpCase Study II - Weather (Part 2)50 xpStep 1: clean_weather()100 xpStep 2: Historical data100 xpStep 3: Present data100 xpStep 4: Extremes100 xpStep 5: Re-use plotting style100 xpWrap-up50 xp

Datasets

Movies (subset of 10000 observations)Test datasetsMammalsAfricaUS CitiesUS StatesGermany unemployment dataPopulation of JapanShape filesParis weather dataReykavik weather dataNew York weather dataLondon weather dataCollaborators

Filip SchouwenaarsPrerequisites

Data Visualization with ggplot2 (Part 2)Rick Scavetta is a co-founder of Scavetta Academy.

Rick Scavetta is a biologist, workshop trainer, freelance data scientist and co-founder of Scavetta Academy, a company dedicated to helping scientists better understand and visualize their data. Rick's practical, hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.

“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyds Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics, USAA

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).